Surbhi Goel

Surbhi Goel

Assistant Professor

University of Pennsylvania, Philadelphia

I am the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. I am associated with the theory group, the ASSET Center on safe, explainable, and trustworthy AI systems, and the Warren Center for network and data sciences.

My research interests lie at the intersection of theoretical computer science and machine learning, with a focus on developing theoretical foundations for modern machine learning paradigms especially deep learning.

Prior to this, I was a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group. I obtained my Ph.D. in the Computer Science department at the University of Texas at Austin advised by Adam Klivans. My dissertation was awarded UTCS’s Bert Kay Dissertation award. My Ph.D. research was generously supported by the JP Morgan AI Fellowship and several fellowships from UT Austin. During my PhD, I visited IAS for the Theoretical Machine learning program and the Simons Institute for the Theory of Computing at UC Berkeley for the Foundations of Deep Learning program (supported by the Simons-Berkeley Research Fellowship). Before that, I received my Bachelors degree from Indian Institute of Technology (IIT) Delhi majoring in Computer Science and Engineering.

For prospective students who are interested in working with me: Please apply to the CIS PhD program and list me as a potential advisor. Unfortunately I will not be able to respond to individual emails from prospective PhD applicants at this time. If you are a current UPenn student looking to do an independent research project, send me an email with your CV, an overview of your research interests, and a brief description of 1-2 recent papers (not mine) you have read and enjoyed. I do not have any current opportunities for external students.

In Fall 2023, I will teach a special topics course CIS 7000: Foundations of Modern Machine Learning: Theory and Empirics. In Spring 2023, I co-taught CIS 5200: Machine Learning with Eric Wong.

Download my resumé.

Interests
  • Theory
  • Machine Learning
Education
  • PhD in Computer Science, 2020

    University of Texas at Austin

  • MS in Computer Science, 2019

    University of Texas at Austin

  • BTech in Computer Science and Engineering, 2015

    Indian Institute of Technology, Delhi

Recent Publications & Preprints

(2023). Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck. NeurIPS 2023 [Spotlight].

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(2023). Adversarial Resilience in Sequential Prediction via Abstention. NeurIPS 2023.

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(2023). Exposing Attention Glitches with Flip-Flop Language Modeling. NeurIPS 2023 [Spotlight].

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(2023). Learning Narrow One-Hidden-Layer ReLU Networks. COLT 2023.

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(2022). Transformers Learn Shortcuts to Automata. ICLR 2023 [notable-top-5%].

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(2022). Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. NeurIPS 2022.

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Outreach

Co-organizer
Co-founded this community building and mentorship initiative for the learning theory community. Co-organized mentorship workshops at ALT 2021, COLT 2021, ALT 2022, and Fall 2022 (with FODSI). Co-organized a graduate applications support program in collaboration with WiML-T.
Mentor

Professional Services

Virtual Experience Chair
Co-organzier of the Mathematics for Modern Machine Learning (M3L) Workshop
Program Committee
Program Committee
Program Committee
Virtual Experience Chair
Co-organized the virtual part of the hybrid conference, including the 2-day virtual-only program.
Program Committee
Program Committee
Treasurer